CN103701669A - Service type detection method and device - Google Patents
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Abstract
The invention discloses a service type detection method and a service type detection device, and belongs to the field of satellite mobile communication. The method comprises the following steps of acquiring first message information in first service data if a service type corresponding to message header information is not found, and acquiring the second message information of second service data of a service type detected within a preset time period before current time; calculating first similarity between the first message information and each piece of second message information, and judging the minimum first similarity is more than or equal to preset similarity or not; if the maximum first similarity is more than or equal to the preset similarity, determining the service type of the second service data corresponding to the second message information corresponding to the maximum first similarity as the service type of the first service data. According to the method and the device, calculation for the detection of the service type in other ways when the service type is not detected in the message header information of the service data is reduced, so that the service type detection efficiency is improved.
Description
Technical field
The present invention relates to satellite mobile communication field, particularly a kind of method and device that detects type of service.Background technology
Satellite mobile communication testing authentication system is the key components of satellite mobile communication system.Satellite mobile communication testing authentication system need to be transmitted the business datum that comprises the artificial services such as multi code Rate of Chinese character speech data, short message data and grouped data between business simulating platform and business simulating terminal.The type of service of the business datum that detection business simulating terminal is received is to detect an important process of satellite mobile communication system.
In prior art, detect the mode of the type of service of business datum, first in the heading information by the business datum to be detected, whether store terminal address or service identification, by these two parameters, can in the corresponding relation of pre-stored terminal address or service identification and type of service, determine type of service.And business datum to be detected in transmitting procedure due to the phenomenon of meeting generation packet loss, therefore the heading information of business datum to be detected may be lost, and now cannot therefrom get the information that detects type of service.Therefore can also extract service feature by the message information in business datum to be detected, and the pre-stored service feature storehouse of the service feature of extraction is contrasted and identified, by type of service corresponding to service feature matching, be defined as the type of service of business datum to be detected in service feature storehouse.
In realizing process of the present invention, inventor finds that prior art at least exists following problem:
In carrying out the matching process of service feature, the service feature that need to mate a plurality of types, the matching process of these service features needs a large amount of calculating, and these computational processes are to expend a large amount of time, cause detection time excessive, can be greater than sometimes the time of transmitting data between business simulating platform and business simulating terminal detection time, make the detection efficiency of type of service and low.
Summary of the invention
In order to solve the problem of prior art, the embodiment of the present invention provides a kind of method and device that detects type of service.Described technical scheme is as follows:
On the one hand, provide a kind of method that detects type of service, described method comprises:
Obtain the heading information in the first business datum to be detected, and inquire about type of service corresponding to described heading information in the corresponding relation of pre-stored heading information and type of service;
If do not inquire type of service corresponding to described heading information, obtain the first message information in described the first business datum, and obtain the second message information of the second business datum that detects type of service in Preset Time section before current time, wherein said the second business datum at least comprises one or more;
Calculate described the first message information and the first similarity between the second message information described in each, and judgement calculate after the first maximum similarity whether be more than or equal to default similarity;
If the first similarity of described maximum is more than or equal to default similarity, the type of service of the second business datum corresponding to the second message information corresponding to the first similarity of described maximum is defined as to the type of service of described the first business datum.
Preferably, after after described judgement calculating, whether the first maximum similarity is greater than default similarity, described method also comprises:
If the first similarity of described maximum is less than default similarity, in described the first message information, obtain the service feature of default plurality of classes;
In service feature set corresponding to each class of service in pre-stored service feature storehouse, calculate respectively the service feature and every kind of second similarity that class of service is corresponding of the every kind getting;
According to the service feature of described every kind and every kind of second similarity that class of service is corresponding, calculate respectively the Weighted Similarity sum that described the first message information corresponds to every kind of class of service;
Choose maximum Weighted Similarity sum, and judge whether the Weighted Similarity sum of described maximum is more than or equal to default Weighted Similarity;
If the Weighted Similarity sum of described maximum is more than or equal to default Weighted Similarity, the type of service using class of service corresponding to the Weighted Similarity sum of described maximum as described the first business datum.
Preferably, after whether the described Weighted Similarity sum that judges described maximum is greater than default Weighted Similarity, described method also comprises:
If the Weighted Similarity sum of described maximum is less than default Weighted Similarity, in described service feature storehouse, create a class of service, and the service feature of every kind corresponding to described the first message information is recorded in service feature set corresponding to the class of service of described establishment.
Preferably, described the first message information of described calculating and the first similarity between the second message information described in each, comprising:
The second message information described in described the first message information and each is carried out to fast Fourier transform, and carry out product accumulation, carry out afterwards that inversefouriertransform obtains described the first message information and the first similarity between the second message information described in each.
Preferably, described in obtain the heading information in the first business datum to be detected before, described method also comprises:
By high-speed data acquisition card, from business emulation terminal, gather the first business datum to be detected;
The business datum that lacks preset field in described the first business datum is abandoned, and described the first business datum is positioned in data buffer storage queue, wait the pending flow process that described the first business datum is carried out to business detection.
On the other hand, provide a kind of device that detects type of service, described device comprises:
Enquiry module for obtaining the heading information of the first business datum to be detected, and is inquired about type of service corresponding to described heading information in the corresponding relation of pre-stored heading information and type of service;
The first acquisition module, if for not inquiring type of service corresponding to described heading information, obtain the first message information in described the first business datum, and obtain the second message information of the second business datum that detects type of service in Preset Time section before current time, wherein said the second business datum at least comprises one or more;
The first judge module, for calculating described the first message information and the first similarity between the second message information described in each, and judgement calculate after the first maximum similarity whether be more than or equal to default similarity;
The first determination module, if be more than or equal to default similarity for the first similarity of described maximum, the type of service of the second business datum corresponding to the second message information corresponding to the first similarity of described maximum is defined as to the type of service of described the first business datum.
Preferably, described device also comprises:
The second acquisition module if be less than default similarity for the first similarity of described maximum, obtains the service feature of default plurality of classes in described the first message information;
The first computing module, in service feature set corresponding to each class of service in the service feature storehouse pre-stored, calculates respectively the service feature and every kind of second similarity that class of service is corresponding of the every kind getting;
The second computing module, for according to the service feature of described every kind and every kind of second similarity that class of service is corresponding, calculates respectively the Weighted Similarity sum that described the first message information corresponds to every kind of class of service;
The second judge module, for choosing maximum Weighted Similarity sum, and judges whether the Weighted Similarity sum of described maximum is more than or equal to default Weighted Similarity;
The second determination module, if be more than or equal to default Weighted Similarity for the Weighted Similarity sum of described maximum, the type of service using class of service corresponding to the Weighted Similarity sum of described maximum as described the first business datum.
Preferably, described device also comprises:
Logging modle, if the Weighted Similarity sum for described maximum is less than default Weighted Similarity, in described service feature storehouse, create a class of service, and the service feature of every kind corresponding to described the first message information is recorded in service feature set corresponding to the class of service of described establishment.
Preferably, described the first judge module specifically for:
The second message information described in described the first message information and each is carried out to fast Fourier transform, and carry out product accumulation, carry out afterwards that inversefouriertransform obtains described the first message information and the first similarity between the second message information described in each.
Preferably, described device also comprises:
Acquisition module, for gathering the first business datum to be detected by high-speed data acquisition card from business emulation terminal;
Pretreatment module, abandons for the business datum that described the first business datum is lacked to preset field, and described the first business datum is positioned in data buffer storage queue, waits the pending flow process that described the first business datum is carried out to business detection.
The beneficial effect that the technical scheme that the embodiment of the present invention provides is brought is:
While type of service not detected in the heading information by the business datum to be detected, by detecting the similarity of the message information that in business datum to be detected, message information is corresponding with the business datum that detects type of service in Preset Time section, and similarity is greater than to the type of service of the business datum of presetting similarity as the type of service of business datum to be detected.Reduce the amount of calculation that detects by other means type of service while type of service being detected in the heading information in business datum not, improved the efficiency that detects type of service.
Accompanying drawing explanation
In order to be illustrated more clearly in the technical scheme in the embodiment of the present invention, below the accompanying drawing of required use during embodiment is described is briefly described, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skills, do not paying under the prerequisite of creative work, can also obtain according to these accompanying drawings other accompanying drawing.
Fig. 1 is the method flow diagram of the detection type of service that provides of the embodiment of the present invention one;
Fig. 2 is the method flow diagram of the detection type of service that provides of the embodiment of the present invention two;
Fig. 3 is the apparatus structure schematic diagram of the detection type of service that provides of the embodiment of the present invention three.
Embodiment
For making the object, technical solutions and advantages of the present invention clearer, below in conjunction with accompanying drawing, embodiment of the present invention is described further in detail.
Embodiment mono-
The embodiment of the present invention provides a kind of method that detects type of service, and referring to Fig. 1, method flow comprises:
101: obtain the heading information in the first business datum to be detected, and inquire about type of service corresponding to described heading information in the corresponding relation of pre-stored heading information and type of service;
102: if do not inquire type of service corresponding to described heading information, obtain the first message information in described the first business datum, and obtain the second message information of the second business datum that detects type of service in Preset Time section before current time, wherein said the second business datum at least comprises one or more;
103: calculate described the first message information and the first similarity between the second message information described in each, and judgement calculate after the first maximum similarity whether be more than or equal to default similarity;
104: if the first similarity of described maximum is more than or equal to default similarity, the type of service of the second business datum corresponding to the second message information corresponding to the first similarity of described maximum is defined as to the type of service of described the first business datum.
While type of service not detected in the heading information of the embodiment of the present invention by the business datum to be detected, by detecting the similarity of the message information that in business datum to be detected, message information is corresponding with the business datum that detects type of service in Preset Time section, and similarity is greater than to the type of service of the business datum of presetting similarity as the type of service of business datum to be detected.Reduce the amount of calculation that detects by other means type of service while type of service being detected in the heading information in business datum not, improved the efficiency that detects type of service.
Embodiment bis-
The embodiment of the present invention provides a kind of method that detects type of service, and referring to Fig. 2, method flow comprises:
201: by high-speed data acquisition card, from business emulation terminal, gather the first business datum to be detected;
This step completes the preliminary treatment to the business datum of artificial service, first starts by the high-speed data acquisition card in business simulating platform, obtains the business datum in each business simulating terminal.
202: the business datum that lacks preset field in described the first business datum is abandoned, and described the first business datum is positioned in data buffer storage queue, wait the pending flow process that described the first business datum is carried out to business detection.
The business datum getting needs to carry out the first business datum of subsequent detection type of service.First carry out data filtering, abandon the business datum that lacks significant data in the first business datum, wherein by judge whether to exist the mode of complete preset field to determine whether certain significant data in the first business datum.
After the step of data filtering is carried out, all the first business datums that get are positioned over to data buffer storage queue and carry out buffer memory.Business simulating platform can sequenced extraction the first business datum also detect one by one from this queue.
203: obtain the heading information in the first business datum to be detected, and inquire about type of service corresponding to described heading information in the corresponding relation of pre-stored heading information and type of service.
Wherein, obtaining the first business datum to be detected is to get from the data buffer storage queue of business emulation platform.
For the process that detects the type of service of the first business datum, first by judging that the heading information of the first business datum starts, the corresponding relation of pre-stored heading information and type of service in business simulating platform.
Wherein, the corresponding relation of heading information and type of service can comprise two classes:
The first kind: the address of business simulating terminal and the corresponding relation of type of service.
Concrete, in heading information, preserve the address of business simulating terminal, by parsing the address of the business simulating terminal in heading information, and search in the corresponding relation of this first kind.
Equations of The Second Kind: the corresponding relation of service identification and type of service.
Concrete, in heading information, preserve service identification, by parsing the service identification in heading information, and search in the corresponding relation of this Equations of The Second Kind.
Wherein, specifically in above-mentioned two class corresponding relations, the process of inquiry and coupling can be:
Heading information detect to adopt the detection of sliding of the sliding window of 8 bits.The first business datum comparison that sliding window is selected and pre-stored above-mentioned two class Elaeocarpus decipiens relations, can carry out preliminary identification to type of service.
Above-mentioned two class corresponding relations can carry out detecting in heading information the type of service of this first business datum jointly, also only use a wherein class corresponding relation to detect, and at this, for occupation mode, do not limit.
The situation that has the type of service that the first business datum do not detected in testing process occurs, and the reason of this situation has two kinds:
The first situation, the heading information in the first business datum is lost or uncomplete content in transmitting procedure, can cause the situation of the type of service that the first business datum cannot be detected to occur;
The second situation, the type of service of the first business datum is new type of service, therefore the type of service all not existing in two kinds of corresponding relations of the above-mentioned first kind and Equations of The Second Kind can cause the situation of the type of service that the first business datum cannot be detected to occur.
Above-mentioned two situations need to enter follow-up further testing process after occurring.
204: if do not inquire type of service corresponding to described heading information, obtain the first message information in described the first business datum, and obtain the second message information of the second business datum that detects type of service in Preset Time section before current time, wherein said the second business datum at least comprises one or more.
Wherein message information is the data payload part in business datum, by the first message information in the first business datum being contrasted to the similarity of the second message information in the second business datum that detects type of service before, can determine the type of the first business datum.
205: calculate described the first message information and the first similarity between the second message information described in each, and judgement calculate after the first maximum similarity whether be more than or equal to default similarity.If be more than or equal to default similarity, perform step 206; If be less than default similarity, perform step 207.
Wherein, the method that similarity is calculated is:
The second message information described in described the first message information and each is carried out to fast Fourier transform, and carry out product accumulation, carry out afterwards that inversefouriertransform obtains described the first message information and the first similarity between the second message information described in each.
206: if the first similarity of described maximum is more than or equal to default similarity, the type of service of the second business datum corresponding to the second message information corresponding to the first similarity of described maximum is defined as to the type of service of described the first business datum.
207: if the first similarity of described maximum is less than default similarity, in described the first message information, obtain the service feature of default plurality of classes.
Now, enter the third testing process, the flow process service feature being detected.
Wherein, the service feature of default plurality of classes can include but not limited to following content:
The control information symbol of the message of business datum, data frame structure, quaternary group information, block length, service transmission rate, business duration, traffic envelope etc.Wherein, data frame structure, quaternary group information, block length can be passed through 128bit sliding window quick-searching; Service transmission rate, business duration can obtain by timer; Traffic envelope can Hanning window be set and fast Fourier transform obtains.
208: in service feature set corresponding to each class of service in pre-stored service feature storehouse, calculate respectively the service feature and every kind of second similarity that class of service is corresponding of the every kind getting.
Pre-stored in business simulating platform have a service feature storehouse, wherein stores respectively the service feature set that service feature of all categories is corresponding.In each service feature set, can store all service features of collecting corresponding to type of service.
By each service feature corresponding to the first business datum carried out to similarity calculating in service feature set corresponding to each class of service, determine the second similarity in the service feature set that each service feature is corresponding with each class of service.
209: according to the service feature of described every kind and every kind of second similarity that class of service is corresponding, calculate respectively the Weighted Similarity sum that described the first message information corresponds to every kind of class of service.
By belonging to each service feature corresponding to the first business datum corresponding to same class of service, be weighted, ask Weighted Similarity sum.Can obtain the Weighted Similarity sum of every kind of class of service corresponding to the first business datum.
210: choose maximum Weighted Similarity sum, and judge whether the Weighted Similarity sum of described maximum is more than or equal to default Weighted Similarity; If maximum Weighted Similarity sum is more than or equal to default Weighted Similarity, perform step 211; If maximum Weighted Similarity sum is less than default Weighted Similarity, perform step 212.
211: if the Weighted Similarity sum of described maximum is more than or equal to default Weighted Similarity, the type of service using class of service corresponding to the Weighted Similarity sum of described maximum as described the first business datum.
212: if the Weighted Similarity sum of described maximum is less than default Weighted Similarity, in described service feature storehouse, create a class of service, and the service feature of every kind corresponding to described the first message information is recorded in service feature set corresponding to the class of service of described establishment.
Now, if meet maximum Weighted Similarity sum, be less than default Weighted Similarity, determine that the first business datum does not detect class of service, now can enter learning phase, the service feature of the first message information in this undetected first business datum is carried out to record, for after detection business datum time identification.
While type of service not detected in the heading information of the embodiment of the present invention by the business datum to be detected, by detecting the similarity of the message information that in business datum to be detected, message information is corresponding with the business datum that detects type of service in Preset Time section, and similarity is greater than to the type of service of the business datum of presetting similarity as the type of service of business datum to be detected.Reduce the amount of calculation that detects by other means type of service while type of service being detected in the heading information in business datum not, improved the efficiency that detects type of service.
Further, while not passing through the type of similarity calculative determination business datum, the service feature corresponding with each pre-stored type of service by the service feature of business datum carries out similarity calculating, and the similarity sum of the mode computing service data by weighting, and similarity sum is greater than to the pre-stored type of service of default similarity sum as the type of service of business datum to be detected.By the judgement of similarity sum, determine more accurately type of service.
If similarity sum is less than default similarity sum, determines that this type of service is new business, and store this new business.Realized the process of the type of service that automatic learning is new.
Embodiment tri-
The embodiment of the present invention provides a kind of device that detects type of service, and referring to Fig. 3, this device comprises:
Enquiry module 301 for obtaining the heading information of the first business datum to be detected, and is inquired about type of service corresponding to described heading information in the corresponding relation of pre-stored heading information and type of service;
The first acquisition module 302, if for not inquiring type of service corresponding to described heading information, obtain the first message information in described the first business datum, and obtain the second message information of the second business datum that detects type of service in Preset Time section before current time, wherein said the second business datum at least comprises one or more;
The first judge module 303, for calculating described the first message information and the first similarity between the second message information described in each, and judgement calculate after the first maximum similarity whether be more than or equal to default similarity;
The first determination module 304, if be more than or equal to default similarity for the first similarity of described maximum, the type of service of the second business datum corresponding to the second message information corresponding to the first similarity of described maximum is defined as to the type of service of described the first business datum.
Wherein, described device also comprises:
The second acquisition module 305 if be less than default similarity for the first similarity of described maximum, obtains the service feature of default plurality of classes in described the first message information;
The first computing module 306, in service feature set corresponding to each class of service in the service feature storehouse pre-stored, calculates respectively the service feature and every kind of second similarity that class of service is corresponding of the every kind getting;
The second computing module 307, for according to the service feature of described every kind and every kind of second similarity that class of service is corresponding, calculates respectively the Weighted Similarity sum that described the first message information corresponds to every kind of class of service;
The second judge module 308, for choosing maximum Weighted Similarity sum, and judges whether the Weighted Similarity sum of described maximum is more than or equal to default Weighted Similarity;
The second determination module 309, if be more than or equal to default Weighted Similarity for the Weighted Similarity sum of described maximum, the type of service using class of service corresponding to the Weighted Similarity sum of described maximum as described the first business datum.
Wherein, described device also comprises:
Logging modle 310, if the Weighted Similarity sum for described maximum is less than default Weighted Similarity, in described service feature storehouse, create a class of service, and the service feature of every kind corresponding to described the first message information is recorded in service feature set corresponding to the class of service of described establishment.
Wherein, described the first judge module 303 specifically for:
The second message information described in described the first message information and each is carried out to fast Fourier transform, and carry out product accumulation, carry out afterwards that inversefouriertransform obtains described the first message information and the first similarity between the second message information described in each.
Wherein, described device also comprises:
Acquisition module 311, for gathering the first business datum to be detected by high-speed data acquisition card from business emulation terminal;
Pretreatment module 312, abandons for the business datum that described the first business datum is lacked to preset field, and described the first business datum is positioned in data buffer storage queue, waits the pending flow process that described the first business datum is carried out to business detection.
While type of service not detected in the heading information of the embodiment of the present invention by the business datum to be detected, by detecting the similarity of the message information that in business datum to be detected, message information is corresponding with the business datum that detects type of service in Preset Time section, and similarity is greater than to the type of service of the business datum of presetting similarity as the type of service of business datum to be detected.Reduce the amount of calculation that detects by other means type of service while type of service being detected in the heading information in business datum not, improved the efficiency that detects type of service.
Further, while not passing through the type of similarity calculative determination business datum, the service feature corresponding with each pre-stored type of service by the service feature of business datum carries out similarity calculating, and the similarity sum of the mode computing service data by weighting, and similarity sum is greater than to the pre-stored type of service of default similarity sum as the type of service of business datum to be detected.By the judgement of similarity sum, determine more accurately type of service.
If similarity sum is less than default similarity sum, determines that this type of service is new business, and store this new business.Realized the process of the type of service that automatic learning is new.
The invention described above embodiment sequence number, just to describing, does not represent the quality of embodiment.
One of ordinary skill in the art will appreciate that all or part of step that realizes above-described embodiment can complete by hardware, also can come the hardware that instruction is relevant to complete by program, described program can be stored in a kind of computer-readable recording medium, the above-mentioned storage medium of mentioning can be read-only memory, disk or CD etc.
The foregoing is only preferred embodiment of the present invention, in order to limit the present invention, within the spirit and principles in the present invention not all, any modification of doing, be equal to replacement, improvement etc., within all should being included in protection scope of the present invention.
Claims (10)
1. a method that detects type of service, is characterized in that, described method comprises:
Obtain the heading information in the first business datum to be detected, and inquire about type of service corresponding to described heading information in the corresponding relation of pre-stored heading information and type of service;
If do not inquire type of service corresponding to described heading information, obtain the first message information in described the first business datum, and obtain the second message information of the second business datum that detects type of service in Preset Time section before current time, wherein said the second business datum at least comprises one or more;
Calculate described the first message information and the first similarity between the second message information described in each, and judgement calculate after the first maximum similarity whether be more than or equal to default similarity;
If the first similarity of described maximum is more than or equal to default similarity, the type of service of the second business datum corresponding to the second message information corresponding to the first similarity of described maximum is defined as to the type of service of described the first business datum.
2. method according to claim 1, is characterized in that, after after described judgement calculating, whether the first maximum similarity is greater than default similarity, described method also comprises:
If the first similarity of described maximum is less than default similarity, in described the first message information, obtain the service feature of default plurality of classes;
In service feature set corresponding to each class of service in pre-stored service feature storehouse, calculate respectively the service feature and every kind of second similarity that class of service is corresponding of the every kind getting;
According to the service feature of described every kind and every kind of second similarity that class of service is corresponding, calculate respectively the Weighted Similarity sum that described the first message information corresponds to every kind of class of service;
Choose maximum Weighted Similarity sum, and judge whether the Weighted Similarity sum of described maximum is more than or equal to default Weighted Similarity;
If the Weighted Similarity sum of described maximum is more than or equal to default Weighted Similarity, the type of service using class of service corresponding to the Weighted Similarity sum of described maximum as described the first business datum.
3. method according to claim 2, is characterized in that, after whether the described Weighted Similarity sum that judges described maximum is greater than default Weighted Similarity, described method also comprises:
If the Weighted Similarity sum of described maximum is less than default Weighted Similarity, in described service feature storehouse, create a class of service, and the service feature of every kind corresponding to described the first message information is recorded in service feature set corresponding to the class of service of described establishment.
4. method according to claim 1, is characterized in that, described the first message information of described calculating and the first similarity between the second message information described in each, comprising:
The second message information described in described the first message information and each is carried out to fast Fourier transform, and carry out product accumulation, carry out afterwards that inversefouriertransform obtains described the first message information and the first similarity between the second message information described in each.
5. method according to claim 1, is characterized in that, described in obtain the heading information in the first business datum to be detected before, described method also comprises:
By high-speed data acquisition card, from business emulation terminal, gather the first business datum to be detected;
The business datum that lacks preset field in described the first business datum is abandoned, and described the first business datum is positioned in data buffer storage queue, wait the pending flow process that described the first business datum is carried out to business detection.
6. a device that detects type of service, is characterized in that, described device comprises:
Enquiry module for obtaining the heading information of the first business datum to be detected, and is inquired about type of service corresponding to described heading information in the corresponding relation of pre-stored heading information and type of service;
The first acquisition module, if for not inquiring type of service corresponding to described heading information, obtain the first message information in described the first business datum, and obtain the second message information of the second business datum that detects type of service in Preset Time section before current time, wherein said the second business datum at least comprises one or more;
The first judge module, for calculating described the first message information and the first similarity between the second message information described in each, and judgement calculate after the first maximum similarity whether be more than or equal to default similarity;
The first determination module, if be more than or equal to default similarity for the first similarity of described maximum, the type of service of the second business datum corresponding to the second message information corresponding to the first similarity of described maximum is defined as to the type of service of described the first business datum.
7. device according to claim 6, is characterized in that, described device also comprises:
The second acquisition module if be less than default similarity for the first similarity of described maximum, obtains the service feature of default plurality of classes in described the first message information;
The first computing module, in service feature set corresponding to each class of service in the service feature storehouse pre-stored, calculates respectively the service feature and every kind of second similarity that class of service is corresponding of the every kind getting;
The second computing module, for according to the service feature of described every kind and every kind of second similarity that class of service is corresponding, calculates respectively the Weighted Similarity sum that described the first message information corresponds to every kind of class of service;
The second judge module, for choosing maximum Weighted Similarity sum, and judges whether the Weighted Similarity sum of described maximum is more than or equal to default Weighted Similarity;
The second determination module, if be more than or equal to default Weighted Similarity for the Weighted Similarity sum of described maximum, the type of service using class of service corresponding to the Weighted Similarity sum of described maximum as described the first business datum.
8. device according to claim 7, is characterized in that, described device also comprises:
Logging modle, if the Weighted Similarity sum for described maximum is less than default Weighted Similarity, in described service feature storehouse, create a class of service, and the service feature of every kind corresponding to described the first message information is recorded in service feature set corresponding to the class of service of described establishment.
9. device according to claim 6, is characterized in that, described the first judge module specifically for:
The second message information described in described the first message information and each is carried out to fast Fourier transform, and carry out product accumulation, carry out afterwards that inversefouriertransform obtains described the first message information and the first similarity between the second message information described in each.
10. device according to claim 6, is characterized in that, described device also comprises:
Acquisition module, for gathering the first business datum to be detected by high-speed data acquisition card from business emulation terminal;
Pretreatment module, abandons for the business datum that described the first business datum is lacked to preset field, and described the first business datum is positioned in data buffer storage queue, waits the pending flow process that described the first business datum is carried out to business detection.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107077769A (en) * | 2017-01-10 | 2017-08-18 | 深圳怡化电脑股份有限公司 | A kind of method and system for recognizing type of service |
| CN110445636A (en) * | 2019-07-03 | 2019-11-12 | 平安科技（深圳）有限公司 | Micro services method for early warning, device and computer equipment based on management platform |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060050704A1 (en) * | 2004-07-14 | 2006-03-09 | Malloy Patrick J | Correlating packets |
| CN102402681A (en) * | 2010-09-16 | 2012-04-04 | 株式会社东芝 | Sheet processing apparatus and sheet processing method |
| CN102547770A (en) * | 2012-03-22 | 2012-07-04 | 北京邮电大学 | Method and system for achieving wireless propagation models based on gray-level matrix and weighted average |
| CN103414600A (en) * | 2013-07-19 | 2013-11-27 | 华为技术有限公司 | Approximate matching method, related device and communication system |
-
2013
- 2013-12-30 CN CN201310745728.2A patent/CN103701669B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060050704A1 (en) * | 2004-07-14 | 2006-03-09 | Malloy Patrick J | Correlating packets |
| CN102402681A (en) * | 2010-09-16 | 2012-04-04 | 株式会社东芝 | Sheet processing apparatus and sheet processing method |
| CN102547770A (en) * | 2012-03-22 | 2012-07-04 | 北京邮电大学 | Method and system for achieving wireless propagation models based on gray-level matrix and weighted average |
| CN103414600A (en) * | 2013-07-19 | 2013-11-27 | 华为技术有限公司 | Approximate matching method, related device and communication system |
Non-Patent Citations (1)
| Title |
|---|
| W.D.KENWORTHY ET.AL: "Classifying ethernet data packets based on Raw bit Patterns", 《KNOWLEDGE DISCOVERY AND DATA MINING,2010.WKDD"10.THIRD INTERNATIONAL CONFERENCE ON》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107077769A (en) * | 2017-01-10 | 2017-08-18 | 深圳怡化电脑股份有限公司 | A kind of method and system for recognizing type of service |
| WO2018129657A1 (en) * | 2017-01-10 | 2018-07-19 | 深圳怡化电脑股份有限公司 | Service type identification method and system |
| CN110445636A (en) * | 2019-07-03 | 2019-11-12 | 平安科技（深圳）有限公司 | Micro services method for early warning, device and computer equipment based on management platform |
| CN110445636B (en) * | 2019-07-03 | 2022-03-18 | 平安科技（深圳）有限公司 | Management platform-based micro-service early warning method and device and computer equipment |
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